| Name | fold-wrappers JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | None |
| Summary | 3rd party model wrappers for fold. |
| upload_time | 2023-06-20 08:02:16 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT License
Copyright (c) 2022-present Myalo UG (haftungbeschränkt) (Mark Aron Szulyovszky, Daniel Szemerey) <info@dreamfaster.ai>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
financial-machine-learning
forecast
forecasting
machine-learning
models
nowcast
time-series
time-series-classification
time-series-regression
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<p align="center" style="display:flex; width:100%; align-items:center; justify-content:center;">
<a style="margin:2px" href="https://github.com/dream-faster/fold-wrappers/actions/workflows/test-statsforecast.yaml"><img alt="Statsforecast Tests" src="https://github.com/dream-faster/fold-wrappers/actions/workflows/test-statsforecast.yaml/badge.svg"/></a>
<a style="margin:2px" href="https://github.com/dream-faster/fold-wrappers/actions/workflows/test-statsmodels.yaml"><img alt="StatsModels Tests" src="https://github.com/dream-faster/fold-wrappers/actions/workflows/test-statsmodels.yaml/badge.svg"/></a>
<a style="margin:2px" href="https://github.com/dream-faster/fold-wrappers/actions/workflows/test-xgboost.yaml"><img alt="XGBoost Tests" src="https://github.com/dream-faster/fold-wrappers/actions/workflows/test-xgboost.yaml/badge.svg"/></a>
<a style="margin:2px" href="https://github.com/dream-faster/fold-wrappers/actions/workflows/test-sktime.yaml"><img alt="Sktime Tests" src="https://github.com/dream-faster/fold-wrappers/actions/workflows/test-sktime.yaml/badge.svg"/></a>
<a style="margin:2px" href="https://github.com/dream-faster/fold-wrappers/actions/workflows/test-prophet.yaml"><img alt="Prophet Test" src="https://github.com/dream-faster/fold-wrappers/actions/workflows/test-prophet.yaml/badge.svg"/></a>
<a style="margin:2px" href="https://discord.gg/EKJQgfuBpE"><img alt="Discord Community" src="https://img.shields.io/badge/Discord-%235865F2.svg?logo=discord&logoColor=white"></a>
<a style="margin:2px" href="https://calendly.com/nowcasting/consultation"><img alt="Calendly Booking" src="https://shields.io/badge/-Speak%20with%20us-orange?logo=minutemailer&logoColor=white"></a>
</p>
<!-- PROJECT LOGO -->
<br />
<div align="center">
<a href="https://dream-faster.github.io/fold/">
<img src="https://raw.githubusercontent.com/dream-faster/fold-wrappers/main/docs/images/logo.svg" alt="Logo" width="90" >
</a>
<h3 align="center"><b>fold-wrappers</b><br> <i>(/fold wrappers/)</i></h3>
<p align="center">
<b>Model wrappers for 3rd party libraries.
<br/>To be used with <a href='https://github.com/dream-faster/fold'>Fold.</a> </b><br>
<br/>
<a href="https://dream-faster.github.io/fold-wrappers/"><strong>Explore the docs »</strong></a>
</p>
</div>
<br />
# Available models
| | Name | Link | Supports<br />Online <br />updating | Wrapper Name<br />& Import Location |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------|:------------------------------------------------------:|:-----------------------------------:|----------------------------------------------------------------------------|
| <img alt='StatsDorecast Logo' src='https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png' height=64> | StatsForecast | [GitHub](https://github.com/Nixtla/statsforecast) | ❌ | **WrapStatsForecast**<br />`from fold_wrappers import WrapStatsForecast` |
| <img alt='NeuralForecast Logo' src='https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png' height=64> | NeuralForecast (beta) | [GitHub](https://github.com/Nixtla/neuralforecast) | ❌ | **WrapNeuralForecast**<br />`from fold_wrappers import WrapNeuralForecast` |
| <img alt='XGBoost Logo' src='https://camo.githubusercontent.com/0ea6e7814dd771f740509bbb668d251d485a6e21f12e287be7cc2275e0eab1d1/68747470733a2f2f7867626f6f73742e61692f696d616765732f6c6f676f2f7867626f6f73742d6c6f676f2e737667' height=64> | XGBoost | [GitHub](https://github.com/dmlc/xgboost) | ✅ | **WrapXGB**<br />`from fold_wrappers import WrapXGB` |
| <img alt='LightGBM Logo' src='https://lightgbm.readthedocs.io/en/latest/_images/LightGBM_logo_black_text.svg' height=64> | LightGBM | [GitHub](https://github.com/Microsoft/LightGBM) | ✅ | **WrapLGBM**<br />`from fold_wrappers import WrapLGBM` |
| <img alt='Sktime Logo' src='https://github.com/sktime/sktime/raw/main/docs/source/images/sktime-logo.jpg?raw=true' height=64> | SKTime (beta) | [GitHub](https://github.com/sktime/sktime) | ✅ | **WrapSktime**<br />`from fold_wrappers import WrapSktime` |
| <img alt='Statsmodels Logo' src='https://github.com/statsmodels/statsmodels/raw/main/docs/source/images/statsmodels-logo-v2-horizontal.svg' width=160> | Statsmodels | [GitHub](https://github.com/statsmodels/statsmodels) | ✅ | **WrapStatsModels**<br />`from fold_wrappers import WrapStatsModels` |
| <img alt='Prophet Logo' src='https://miro.medium.com/v2/resize:fit:964/0*tVCene42rgUTNv9Q.png' width=160> | Prophet | [GitHub](https://github.com/facebook/prophet) | ✅ | **WrapProphet**<br />`from fold_wrappers import WrapProphet` |
| <img alt='Scikit-Learn Logo' src='https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/scikit-learn-logo.png' width=160> | Sklearn <br/>(natively available in `fold`) | [GitHub](https://github.com/scikit-learn/scikit-learn) | 🟡<br/>(some) | Sklearn doesn't need to be wrapped,<br />just pass in the models. |
# Installation
- Prerequisites: `python >= 3.7` and `pip`
- Install from pypi:
```
pip install fold-wrappers
```
- Depending on what model you'd like to wrap, you can either install the library directly or run
```
pip install "fold-wrappers[<your_library_name>]"
```
# Quickstart
You can quickly train your chosen models and get predictions by running:
```python
from fold import ExpandingWindowSplitter, train_evaluate
from fold.utils.dataset import get_preprocessed_dataset
from statsforecast.models import ARIMA
from fold_wrappers import WrapStatsForecast
X, y = get_preprocessed_dataset(
"weather/historical_hourly_la", target_col="temperature", shorten=1000
)
model = WrapStatsForecast(
model_class=ARIMA, # Pass in the class
init_args={"order": (1, 0, 0)}, # and the arguments to pass in at `init()`
online_mode=False, # Enable online updates where available
)
splitter = ExpandingWindowSplitter(initial_train_window=0.2, step=50)
scorecard, predictions, trained_pipeline = train_evaluate(model, X, y, splitter)
```
You can also wrap a model that you have initiate first:
```python
wrapped_model = WrapStatsForecast.from_model(
ARIMA(order=(1, 0, 0)),
online_mode=False # Enable online updates where available
)
```
## Our Open-core Time Series Toolkit
[](https://github.com/dream-faster/krisi)
[](https://github.com/dream-faster/fold)
[](https://github.com/dream-faster/fold-models)
[](https://github.com/dream-faster/fold-wrappers)
If you want to try them out, we'd love to hear about your use case and help, [please book a free 30-min call with us](https://calendly.com/nowcasting/consultation)!
## Contribution
Join our [Discord](https://discord.gg/EKJQgfuBpE) for live discussion!
Submit an issue or reach out to us on info at dream-faster.ai for any inquiries.
## Licence & Usage
Fold-wrappers is under the MIT Licence, but `fold` is not. [Read more](https://dream-faster.github.io/fold/product/license/)
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"description": "<p align=\"center\" style=\"display:flex; width:100%; align-items:center; justify-content:center;\">\n <a style=\"margin:2px\" href=\"https://github.com/dream-faster/fold-wrappers/actions/workflows/test-statsforecast.yaml\"><img alt=\"Statsforecast Tests\" src=\"https://github.com/dream-faster/fold-wrappers/actions/workflows/test-statsforecast.yaml/badge.svg\"/></a>\n <a style=\"margin:2px\" href=\"https://github.com/dream-faster/fold-wrappers/actions/workflows/test-statsmodels.yaml\"><img alt=\"StatsModels Tests\" src=\"https://github.com/dream-faster/fold-wrappers/actions/workflows/test-statsmodels.yaml/badge.svg\"/></a>\n <a style=\"margin:2px\" href=\"https://github.com/dream-faster/fold-wrappers/actions/workflows/test-xgboost.yaml\"><img alt=\"XGBoost Tests\" src=\"https://github.com/dream-faster/fold-wrappers/actions/workflows/test-xgboost.yaml/badge.svg\"/></a>\n <a style=\"margin:2px\" 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src=\"https://raw.githubusercontent.com/dream-faster/fold-wrappers/main/docs/images/logo.svg\" alt=\"Logo\" width=\"90\" >\n </a>\n<h3 align=\"center\"><b>fold-wrappers</b><br> <i>(/fold wrappers/)</i></h3>\n <p align=\"center\">\n <b>Model wrappers for 3rd party libraries.\n <br/>To be used with <a href='https://github.com/dream-faster/fold'>Fold.</a> </b><br>\n <br/>\n <a href=\"https://dream-faster.github.io/fold-wrappers/\"><strong>Explore the docs \u00bb</strong></a>\n </p>\n</div>\n<br />\n\n# Available models\n\n| | Name | Link | Supports<br />Online <br />updating | Wrapper Name<br />& Import Location |\n|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------|:------------------------------------------------------:|:-----------------------------------:|----------------------------------------------------------------------------|\n| <img alt='StatsDorecast Logo' src='https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png' height=64> | StatsForecast | [GitHub](https://github.com/Nixtla/statsforecast) | \u274c | **WrapStatsForecast**<br />`from fold_wrappers import WrapStatsForecast` |\n| <img alt='NeuralForecast Logo' src='https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png' height=64> | NeuralForecast (beta) | [GitHub](https://github.com/Nixtla/neuralforecast) | \u274c | **WrapNeuralForecast**<br />`from fold_wrappers import WrapNeuralForecast` |\n| <img alt='XGBoost Logo' src='https://camo.githubusercontent.com/0ea6e7814dd771f740509bbb668d251d485a6e21f12e287be7cc2275e0eab1d1/68747470733a2f2f7867626f6f73742e61692f696d616765732f6c6f676f2f7867626f6f73742d6c6f676f2e737667' height=64> | XGBoost | [GitHub](https://github.com/dmlc/xgboost) | \u2705 | **WrapXGB**<br />`from fold_wrappers import WrapXGB` |\n| <img alt='LightGBM Logo' src='https://lightgbm.readthedocs.io/en/latest/_images/LightGBM_logo_black_text.svg' height=64> | LightGBM | [GitHub](https://github.com/Microsoft/LightGBM) | \u2705 | **WrapLGBM**<br />`from fold_wrappers import WrapLGBM` |\n| <img alt='Sktime Logo' src='https://github.com/sktime/sktime/raw/main/docs/source/images/sktime-logo.jpg?raw=true' height=64> | SKTime (beta) | [GitHub](https://github.com/sktime/sktime) | \u2705 | **WrapSktime**<br />`from fold_wrappers import WrapSktime` |\n| <img alt='Statsmodels Logo' src='https://github.com/statsmodels/statsmodels/raw/main/docs/source/images/statsmodels-logo-v2-horizontal.svg' width=160> | Statsmodels | [GitHub](https://github.com/statsmodels/statsmodels) | \u2705 | **WrapStatsModels**<br />`from fold_wrappers import WrapStatsModels` |\n| <img alt='Prophet Logo' src='https://miro.medium.com/v2/resize:fit:964/0*tVCene42rgUTNv9Q.png' width=160> | Prophet | [GitHub](https://github.com/facebook/prophet) | \u2705 | **WrapProphet**<br />`from fold_wrappers import WrapProphet` |\n| <img alt='Scikit-Learn Logo' src='https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/scikit-learn-logo.png' width=160> | Sklearn <br/>(natively available in `fold`) | [GitHub](https://github.com/scikit-learn/scikit-learn) | \ud83d\udfe1<br/>(some) | Sklearn doesn't need to be wrapped,<br />just pass in the models. |\n\n# Installation\n\n- Prerequisites: `python >= 3.7` and `pip`\n\n- Install from pypi:\n ```\n pip install fold-wrappers\n ```\n- Depending on what model you'd like to wrap, you can either install the library directly or run\n ```\n pip install \"fold-wrappers[<your_library_name>]\"\n ```\n\n# Quickstart\n\n\n\n\nYou can quickly train your chosen models and get predictions by running:\n\n```python\n from fold import ExpandingWindowSplitter, train_evaluate\n from fold.utils.dataset import get_preprocessed_dataset\n from statsforecast.models import ARIMA\n\n from fold_wrappers import WrapStatsForecast\n\n X, y = get_preprocessed_dataset(\n \"weather/historical_hourly_la\", target_col=\"temperature\", shorten=1000\n )\n model = WrapStatsForecast(\n model_class=ARIMA, # Pass in the class\n init_args={\"order\": (1, 0, 0)}, # and the arguments to pass in at `init()`\n online_mode=False, # Enable online updates where available\n )\n splitter = ExpandingWindowSplitter(initial_train_window=0.2, step=50)\n\n scorecard, predictions, trained_pipeline = train_evaluate(model, X, y, splitter)\n```\n\nYou can also wrap a model that you have initiate first:\n\n```python\nwrapped_model = WrapStatsForecast.from_model(\n ARIMA(order=(1, 0, 0)),\n online_mode=False # Enable online updates where available\n)\n```\n## Our Open-core Time Series Toolkit\n\n[](https://github.com/dream-faster/krisi)\n[](https://github.com/dream-faster/fold)\n[](https://github.com/dream-faster/fold-models)\n[](https://github.com/dream-faster/fold-wrappers)\n\nIf you want to try them out, we'd love to hear about your use case and help, [please book a free 30-min call with us](https://calendly.com/nowcasting/consultation)!\n\n## Contribution\n\nJoin our [Discord](https://discord.gg/EKJQgfuBpE) for live discussion!\n\nSubmit an issue or reach out to us on info at dream-faster.ai for any inquiries.\n\n\n## Licence & Usage\n\nFold-wrappers is under the MIT Licence, but `fold` is not. 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